rmcorr_mat | R Documentation |
Create a repeated measures correlation matrix.
rmcorr_mat(participant, variables, dataset, CI.level = 0.95)
participant |
A variable giving the subject name/id for each observation. |
variables |
A character vector indicating the columns of variables to include in the correlation matrix. |
dataset |
The data frame containing the variables. |
CI.level |
The level of confidence intervals to use in the rmcorr models. |
A list with class "rmcmat" containing the following components.
matrix |
the repeated measures correlation matrix |
summary |
a dataframe showing rmcorr stats for each pair of variables |
models |
a list of the full rmcorr model for each pair of variables |
Bakdash, J.Z., & Marusich, L.R. (2017). Repeated Measures Correlation. Frontiers in Psychology, 8, 456. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3389/fpsyg.2017.00456")}.
Bland, J.M., & Altman, D.G. (1995). Calculating correlation coefficients with repeated observations: Part 1 – correlation within subjects. BMJ, 310, 446, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1136/bmj.310.6977.446")}.
Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences (3rd edition), Routledge. ISBN: 9780805822236.
rmcorr, plot.rmc
dist_rmc_mat <- rmcorr_mat(participant = Subject,
variables = c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching"),
dataset = twedt_dist_measures,
CI.level = 0.95)
plot(dist_rmc_mat$models[[2]])
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